The Efficiency Optimal Control Of The Electric Vehicles' Drive-train Systems

In recent years, along with tenser international energy situation and increasing requirement of high operation performance, the loss minimization control of IM attracts more and more attention. Now, the research of electric vehicles is no doubt a hotspot, whose drive-train system strictly demands both the least losses and fast response.As to the cases such as electric vehicles, which have a high demand not only for energyconservation but also for good control performance, there are three vital problems to solve to realize the optimal control of their drive-train systems. First is how to obtain optimal rotor flux linkage for loss minimization control; Second is to design fast response control strategy to avoid the descending of dynamic performance caused by the flux linkage optimal control; Third is to design high performance closed loop control strategy to make the system keep excellent variable-speed behavior under the situations of time-varied parameters, variable flux linkage and variable load, etc. Under the background of electric vehicles, this paper studies on the loss minimization control strategy and fast response control strategy in detail. Simulations and experiments results prove the validity of these new methods, whilst fruitful innovative results are obtained, which provide effective solutions for the efficiency optimal control of the electric vehicles' drive-train systems.In this thesis, on the basis of detailed analysis of the asynchronous motor's loss behavior and its mathematical model including the iron losses, a kind of loss model control strategy is deduced and the variety law of the optimal flux linkage is illustrated. And then, and a large number of corresponding experiments are completed.Based on gradient algorithm and golden section method, the search controllers are designed to optimize the efficiency of the asynchronous motor to avoid the parameter change's influence to loss minimization control. The simulation results indicate that the convergence speed of golden section method is obviously superior to that of the gradient algorithm; however in the former search process, the fluctuation of flux linkage is not reduced. So the improved golden section algorithm is proposed, the validity of which is verified through theoretical analysis and experiments. Combined the advantages both of loss model controller and the search controller, a novel hybrid loss minimization control strategy is presented. The steps are as follows: first, get the approximate optimal flux linkage according to the loss model; second, find operation point with the minimal input power by on-line search. Simulation and experiment verify that, this hybrid strategy, which has rapid convergence speed and robustness, is a promising method to efficiency optimization of asynchronous motor.In the traditional loss minimization control system, the improvement of the efficiency is at the cost of deterioration of the dynamic response. Aimed at the problem above, a fast response control strategy is studied based on the current dynamic assignment. The experiment results indicate that this method will decrease the dynamic speed dropping and reduce dynamic adjustment time. Simulation results show that, this scheme, which has excellent dynamic performance, notably reduces the speed-fall of loss minimization control system when load changes. It is indeed an efficient solution to the contradiction between the loss minimization and fast response control.